{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/victor/.local/lib/python3.6/site-packages/psycopg2/__init__.py:144: UserWarning: The psycopg2 wheel package will be renamed from release 2.8; in order to keep installing from binary please use \"pip install psycopg2-binary\" instead. For details see: <http://initd.org/psycopg/docs/install.html#binary-install-from-pypi>.\n",
      "  \"\"\")\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "import json\n",
    "import time\n",
    "import itertools\n",
    "from scipy import stats\n",
    "import psycopg2 as psql\n",
    "from psycopg2.extras import RealDictCursor\n",
    "import pickle\n",
    "\n",
    "import sys\n",
    "sys.path.append(\"..\")\n",
    "from tools.flight_projection import *\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(color_codes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_obj(obj, name ):\n",
    "    with open(name + '.pkl', 'wb') as f:\n",
    "        pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)\n",
    "\n",
    "def load_obj(name ):\n",
    "    with open(name + '.pkl', 'rb') as f:\n",
    "        return pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "det_dict = load_obj('deterministic_dict')\n",
    "intent_dict = load_obj('intent_dict')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/victor/.local/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6571: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/home/victor/.local/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6571: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lt = 600\n",
    "\n",
    "det_data = [x for x in det_dict[str(lt)] if not np.isnan(x)]\n",
    "int_data = [x for x in intent_dict[str(lt)] if not np.isnan(x)]\n",
    "\n",
    "det_stdz = (det_data-np.mean(det_data))/np.std(det_data)\n",
    "int_stdz = (int_data-np.mean(int_data))/np.std(int_data)\n",
    "\n",
    "from matplotlib import mlab\n",
    "fig, ax = plt.subplots(figsize=(15, 10))\n",
    "\n",
    "n_bins = 800\n",
    "\n",
    "# n, bins, patches = \n",
    "ax.hist(det_stdz, n_bins, normed=1, histtype='stepfilled',\n",
    "       cumulative=False, label='Deterministic distr. at %s seconds' % lt, alpha=0.6)\n",
    "\n",
    "ax.hist(int_stdz, n_bins, normed=1, histtype='stepfilled',\n",
    "       cumulative=False, label='Intent distr. at %s seconds' % lt, alpha=0.6)\n",
    "    \n",
    "#     plt.xlabel('')\n",
    "plt.legend(prop={'size': 14})\n",
    "plt.xlim(-2,2)\n",
    "# plt.ylim(-500,500)\n",
    "plt.xlabel('Standardized CTE')\n",
    "plt.ylabel('Normalized count')\n",
    "plt.title('Standardized TTC error distributions at different look-ahead times')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5008"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(intent_dict[str(lt)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in [kx for kx in bin_df.keys() if int(kx) <= 1200]:\n",
    "    box_data.append((int(k), [i for i in bin_df[k] if not np.isnan(i)]))\n",
    "    \n",
    "box_data_sort = sorted(box_data, key=lambda tup: tup[0])\n",
    "box_data_2 = [i[1] for i in box_data_sort]\n",
    "\n",
    "x = range(len(box_data_2))\n",
    "\n",
    "plt.figure(figsize=(20,8))\n",
    "plt.boxplot(box_data_2, showfliers=False, patch_artist=True, whis=[5,95])\n",
    "plt.xticks(x, [i[0] for i in box_data_sort])\n",
    "plt.xticks(rotation=70)\n",
    "plt.xlabel('Look-ahead time (seconds)')\n",
    "plt.ylabel('TTC difference in seconds')\n",
    "plt.title('Evolution of TTC error over look-ahead time')\n",
    "plt.show()"
   ]
  }
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